Auditory discrimination predicts linguistic outcome in Italian infants with and without familial risk for language learning impairment
Bibliographic record
Abstract
Infants' ability to discriminate between auditory stimuli presented in rapid succession and differing in fundamental frequency (Rapid Auditory Processing [RAP] abilities) has been shown to be anomalous in infants at familial risk for Language Learning Impairment (LLI) and to predict later language outcomes. This study represents the first attempt to investigate RAP in Italian infants at risk for LLI (FH+), examining two critical acoustic features: frequency and duration, both embedded in a rapidly-presented acoustic environment. RAP skills of 24 FH+ and 32 control (FH-) Italian 6-month-old infants were characterized via EEG/ERP using a multi-feature oddball paradigm. Outcome measures of expressive vocabulary were collected at 20 months. Group differences favoring FH- infants were identified: in FH+ infants, the latency of the N2* peak was delayed and the mean amplitude of the positive mismatch response was reduced, primarily for frequency discrimination and within the right hemisphere. Moreover, both EEG measures were correlated with language scores at 20 months. Results indicate that RAP abilities are atypical in Italian infants with a first-degree relative affected by LLI and that this impacts later linguistic skills. These findings provide a compelling cross-linguistic comparison with previous research on American infants, supporting the biological unity hypothesis of LLI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".